In some data mining applications the analyzed data can be classified as simultaneously belonging to more than one class, this characterizes the multi-label classification problem. Numerous methods for dealing with this problem are based on decomposition, which essentially treats labels (or some subsets of labels) independently and ignores interactions between them. This fact might be a problem, as some labels may be correlated to local patterns in the data. In this paper, we propose to enhance multi-label classifiers with the aid of biclusters, which are capable of finding the correlation between subsets of objects, features and labels. We then construct binary features from these patterns that can be interpreted as local correlations (in terms of subset of features and instances) in the data. These features are used as input for multi-label classifiers. We experimentally show that using such constructed features can improve the classification performance of some decompositive multi-label learning techniques.